remove: redundant documentation files

- Remove README_HF_SPACES.md and DEPLOYMENT_SUMMARY.md
- Keep README.md as single source of documentation
- Streamline project documentation structure

DEPLOYMENT_SUMMARY.md

@@ -1,93 +0,0 @@
-# DataEngEval - Deployment Summary
-
-## 🚀 Ready for Hugging Face Spaces Deployment
-
-### Space Details
-- **Space Name**: `DataEngEval`
-- **URL**: `https://huggingface.co/spaces/your-username/DataEngEval`
-- **SDK**: Gradio
-- **Hardware**: CPU Basic
-
-### ✅ Code Status: READY
-
-#### Required Files Present
-- ✅ `app.py` - Main Gradio application
-- ✅ `requirements.txt` - Lightweight dependencies (no heavy ML libs)
-- ✅ `config/` - All configuration files
-- ✅ `src/` - Source code modules
-- ✅ `tasks/` - Multi-use-case datasets
-- ✅ `prompts/` - SQL templates
-
-#### HF Spaces Optimized
-- ✅ **No heavy dependencies**: No torch, transformers, accelerate
-- ✅ **Remote inference**: Uses Hugging Face Inference API
-- ✅ **Mock mode**: Works without API keys
-- ✅ **Lightweight**: Fast deployment and startup
-
-### 🎯 Multi-Use-Case Support
-
-#### 1. SQL Generation
-- **Dataset**: NYC Taxi Small
-- **Dialects**: Presto, BigQuery, Snowflake
-- **Metrics**: Correctness, execution, result matching
-
-#### 2. Code Generation
-- **Python**: Algorithms, data structures, OOP
-- **Go**: Algorithms, HTTP handlers, concurrency
-- **Metrics**: Syntax, compilation, execution, quality
-
-#### 3. Documentation Generation
-- **Technical Docs**: API docs, function docs, installation guides
-- **API Documentation**: OpenAPI, GraphQL, REST endpoints
-- **Metrics**: Accuracy, completeness, clarity, format compliance
-
-### 🔑 HF_TOKEN Setup
-
-#### Get Your Token
-1. Go to [Hugging Face Settings](https://huggingface.co/settings/tokens)
-2. Click "New token"
-3. Choose "Read" access
-4. Copy the token
-
-#### Add to Space
-1. Go to Space Settings → Secrets
-2. Add `HF_TOKEN` with your token
-3. **Without token**: App works in mock mode (perfect for demos!)
-
-### 🚀 Deployment Steps
-
-#### Option A: Git Push (Recommended)
-```bash
-# Initialize git
-git init
-git add .
-git commit -m "Initial commit for DataEngEval"
-
-# Add HF Space as remote
-git remote add hf https://huggingface.co/spaces/your-username/DataEngEval
-
-# Push to HF
-git push hf main
-```
-
-#### Option B: Direct Upload
-- Upload all files via HF Spaces web interface
-
-### 📊 What You'll Get
-
-#### Without HF_TOKEN (Mock Mode)
-- ✅ Full functionality demonstration
-- ✅ Realistic code generation (mock)
-- ✅ Complete evaluation pipeline
-- ✅ Leaderboard and metrics
-- ✅ Perfect for demos and testing
-
-#### With HF_TOKEN (Real Models)
-- ✅ Real Hugging Face model inference
-- ✅ Actual code generation from models
-- ✅ Production-ready evaluation
-- ✅ Real performance metrics
-
-### 🎉 Ready to Deploy!
-
-Your DataEngEval Space is **100% ready** for deployment! 🚀

README_HF_SPACES.md

@@ -1,197 +0,0 @@
-# Hugging Face Spaces Deployment Guide
-
-This guide explains how to deploy the NL→SQL Leaderboard on Hugging Face Spaces.
-
-## 🚀 Quick Deployment
-
-### Step 1: Create a New Space
-
-1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
-2. Click "Create new Space"
-3. Fill in the details:
-   - **Space name**: `DataEngEval` (or your preferred name)
-   - **License**: Choose appropriate license
-   - **Visibility**: Public or Private
-   - **SDK**: **Gradio**
-   - **Hardware**: CPU Basic (sufficient for this app)
-
-### Step 2: Upload Your Code
-
-#### Option A: Git Clone and Push
-```bash
-# Clone your repository
-git clone <your-repo-url>
-cd dataeng-leaderboard
-
-# Add Hugging Face Space as remote
-git remote add hf https://huggingface.co/spaces/your-username/DataEngEval
-
-# Push to Hugging Face
-git push hf main
-```
-
-#### Option B: Direct Upload
-1. Upload all files to your Space using the web interface
-2. Make sure to include all files from the project structure
-
-### Step 3: Configure Environment (Optional)
-
-1. Go to your Space settings
-2. Add secrets if needed:
-   - `HF_TOKEN`: Your Hugging Face API token (for real model inference)
-3. The app will work without tokens using mock mode
-
-### Step 4: Deploy
-
-The Space will automatically build and deploy. You'll see the URL once ready.
-
-## 📁 Required Files for Deployment
-
-Make sure these files are present in your Space:
-
-```
-├── app.py                     # ✅ Main application
-├── requirements.txt           # ✅ Dependencies
-├── config/
-│   └── models.yaml           # ✅ Model configurations
-├── src/
-│   ├── evaluator.py          # ✅ Evaluation logic
-│   ├── models_registry.py    # ✅ Model interfaces
-│   └── scoring.py            # ✅ Scoring logic
-├── tasks/                    # ✅ Datasets
-│   ├── nyc_taxi_small/
-│   ├── tpch_tiny/
-│   └── ecommerce_orders_small/
-├── prompts/                  # ✅ SQL templates
-│   ├── template_presto.txt
-│   ├── template_bigquery.txt
-│   └── template_snowflake.txt
-└── README.md                 # ✅ Documentation
-```
-
-## 🔧 Configuration
-
-### Model Configuration
-
-Edit `config/models.yaml` to add/remove models:
-
-```yaml
-models:
-  - name: "Your Model"
-    provider: "huggingface"
-    model_id: "your/model-id"
-    params:
-      max_new_tokens: 256
-      temperature: 0.1
-    description: "Your model description"
-```
-
-### Environment Variables
-
-Set these in your Space settings:
-
-- `HF_TOKEN`: Hugging Face API token (optional)
-- `MOCK_MODE`: Set to "true" to force mock mode
-
-## 🚀 Features
-
-### Automatic Features
-- **Auto-deployment**: Changes pushed to Git trigger automatic rebuilds
-- **Persistent storage**: Leaderboard results persist across deployments
-- **Mock mode**: Works without API keys for demos
-- **Remote inference**: No heavy model downloads
-
-### Performance Optimizations
-- Lightweight dependencies
-- Remote model inference
-- Efficient DuckDB execution
-- Minimal memory footprint
-
-## 🐛 Troubleshooting
-
-### Common Issues
-
-**Build fails**: Check that all required files are present and `requirements.txt` is correct
-
-**App doesn't start**: Verify `app.py` is in the root directory
-
-**Models not working**: Check `config/models.yaml` format and model IDs
-
-**Datasets not loading**: Ensure all dataset files are in `tasks/` directory
-
-### Debug Mode
-
-To debug locally before deploying:
-
-```bash
-# Install dependencies
-pip install -r requirements.txt
-
-# Run locally
-gradio app.py
-
-# Test with mock mode
-export MOCK_MODE=true
-gradio app.py
-```
-
-## 📊 Monitoring
-
-### Space Logs
-- Check the "Logs" tab in your Space for runtime errors
-- Monitor memory usage in the "Settings" tab
-
-### Performance
-- CPU usage should be minimal (remote inference)
-- Memory usage should be low (no local models)
-- Response times depend on Hugging Face Inference API
-
-## 🔄 Updates
-
-### Updating Your Space
-1. Make changes to your code
-2. Commit and push to your Space's Git repository
-3. The Space will automatically rebuild
-
-### Adding New Models
-1. Edit `config/models.yaml`
-2. Push changes to your Space
-3. New models will be available immediately
-
-### Adding New Datasets
-1. Create new folder in `tasks/`
-2. Add required files (`schema.sql`, `loader.py`, `cases.yaml`)
-3. Push changes to your Space
-
-## 🎯 Best Practices
-
-### Code Organization
-- Keep all source code in `src/` directory
-- Use relative imports
-- Minimize dependencies in `requirements.txt`
-
-### Performance
-- Use Hugging Face Inference API for models
-- Avoid local model loading
-- Keep datasets small for faster evaluation
-
-### User Experience
-- Provide clear error messages
-- Use mock mode for demos
-- Include comprehensive documentation
-
-## 📚 Additional Resources
-
-- [Hugging Face Spaces Documentation](https://huggingface.co/docs/hub/spaces)
-- [Gradio Documentation](https://gradio.app/docs/)
-- [Hugging Face Inference API](https://huggingface.co/docs/api-inference)
-
-## 🆘 Support
-
-If you encounter issues:
-
-1. Check the Space logs for errors
-2. Verify all required files are present
-3. Test locally before deploying
-4. Check Hugging Face Spaces status page
-5. Review the troubleshooting section above